This study aims to propose a hybrid vibration-based fault detection approach to identify early-stage pitting in bevel gearboxes, aiming to enhance machine reliability and prevent catastrophic failure.
To obtain vibration signals under both healthy and defective conditions, an experimental test rig was designed and constructed. The Discrete Wavelet Transform (DWT) was used to break down the signals up to the fourth level using a Daubechies-4 mother wavelet. The J-48 decision tree approach was used to pick significant statistical features, which were then categorized using an adaptive neuro-fuzzy inference system (ANFIS) model that combines neural learning and fuzzy logic reasoning.
The recommended DWT–ANFIS hybrid approach successfully distinguished between normal, slight, moderate and severe pitting conditions with classification accuracy of 98.01%.
The study focuses on bevel gearboxes; further research may explore their application to other gearbox types and more complex industrial conditions.
The model supports real-time condition monitoring of gearboxes, enabling proactive maintenance and minimizing downtime.
By preventing machinery failure, the approach contributes to workplace safety, energy efficiency and sustainable industrial operations.
To diagnose gearbox faults early, the paper presents an effective hybrid methodology that combines wavelet-based feature extraction, decision-tree-based feature selection and ANFIS classification. This study is innovative because it combines DWT, decision tree-based feature selection and ANFIS classification in a hybrid manner to detect pitting in bevel gearboxes early on, with exceptional accuracy and real-time monitoring capabilities.
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2025-0383/
